Active Inference, Computational Phenomenology, and Advanced Meditation: Toward the Formalization of the Experience of Meditation

Computational phenomenology uses models to explain experiences in advanced meditation, such as enhanced well-being, attentional shifts, and minimal phenomenal experiences. A review finds that precision weighting—how the brain adjusts the influence of sensory signals—is a key shared mechanism across models. Early models emphasize top-down attention toward interoception or specific objects, while later models focus on layer-specific precision re-weighting within the meditator's hierarchical generative model. Few models address increased cognitive flexibility or learning from meditation, and mechanisms behind compassion traditions remain underexplored. Addressing these gaps is crucial for refining computational models of advanced meditation.

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Abstract

Computational phenomenology has emerged as a powerful framework for investigating advanced meditation states and stages, and meditative development and endpoints. Various models have been proposed to mechanistically explain the diverse experiences associated with these practices, including enhanced well-being, attentional shifts, and defabrication, as well as minimal phenomenal experiences and meditative endpoints. However, these models have developed in disparate directions, and an integrative understanding of the underlying mechanisms of advanced meditation remains elusive. This review examines how computational models attempt to account for the phenomenology of advanced meditation, with a particular focus on Active Inference as a modeling framework. We find that a key point of convergence across models is the role of precision weighting as a driver of experiential shifts. Furthermore, we observe a marked difference between early models, which emphasize top-down attentional modulation toward interoception or specific focus objects, and later models which center on layer-specific precision re-weighting within the meditator’s hierarchical generative model. These differences arise from variations in the models' aims, scope, and definitions of contemplative practice. Few models address reported increases in cognitive flexibility and learning from meditation, fundamental mechanisms behind informal practice and affective processes, as well as processes underlying compassion traditions, remain underexplored. Addressing these gaps is crucial for refining computational models of advanced meditation and informing our understanding of its cognitive, affective, and experiential effects.